Forests offer a wide diversity of crucial ecosystem services, including climate and hydrological regulation, preservation of biodiversity, provision of both timber and non-timber products, and opportunities for recreation, among others. However, forests face transformations driven by natural and human factors, which are foreseen to increase under the context of climate-change and socio-economic trends. The monitoring of forests is paramount to promote a sustainable use and to enhance resilience against hazards, in alignment with the goals of the 2030 Agenda.
Insects constitute important catalysts for change within forest ecosystems. They can feed on the phloem (e.g. bark beetles) as well as needles or leaves (e.g. defoliators) of trees, often interacting with other pathogens or inciting additional disturbances (e.g. blowdown). The impacts of insects on host trees span a spectrum from deformation to eventual mortality. One of the major defoliating insects affecting pines and cedars in the Mediterranean region is the pine processionary moth (Thaumetopoea pityocampa), which annually affects over 500,000 ha in Spain (Figure 1). Though the processionary moth typically does not directly result in tree mortality, it significantly impedes tree growth (Figure 2), leading to economic losses in forest plantations. Moreover, its impact is widely known for affecting pets, farm animals and humans, often causing skin reactions and other related issues.
Figure 1. Processionary caterpillar
In practical terms, the monitoring of processionary moth effects on conifer forests remains a challenge. For example, in Spain, approximately 70% of the Autonomous Communities have processionary moth monitoring programs reliant on in situ surveys. While these surveys are valuable, their high costs, limited spatial scope (often confined to stand scale or specific areas) and infrequent temporal intervals (typically conducted once a year) may limit effective forest management actions. The broadening of remote sensing applications in forest ecosystems, coupled with rapid advancement in platforms, sensors, and modelling algorithms, offers quantitative insights that complement in situ surveys while enhancing the spatio-temporal frequency of observations. These data open new perspectives towards creating a holistic vision for a near real-time monitoring of forest ecosystems.
Figure 2. Pine trees defoliated by the pine processionary moth
Our ongoing efforts, framed in OUTBREAK and PROWARM projects, pursue three major Wh- questions, in order to improve processionary moth defoliations monitoring in forests, achieved through the integration of remote sensing tools: “why”, “where” and “when”. These questions aim to assess forest vulnerability to the pine processionary moths and to detect and monitor processionary moth defoliations.
Considering the “Why” aspect, which revolves around forest vulnerability, it is unsurprising that the year-to-year fluctuations in processionary moth impact relies on a combination of intrinsic species characteristics, environmental factors, and forest structure. Our findings reveal that, within the mountain range of Cuenca, pine stands characterized by greater canopy continuity, uniform structure, and higher altitudes (associated with species distribution), exhibit heightened vulnerability. However, stands with lower historical recurrence and greater thermal amplitude of the minimum temperatures are potentially less vulnerable. Thus, the integration of active LiDAR (light detection and ranging) and passive Landsat datasets emerges as a powerful strategy for characterizing forest structure and diversity—two factors that intricately shape pine processionary moth impacts.
Multispectral remote sensing data constitutes a suitable information source for detecting alterations in tree foliage due to natural or human-induced agents. Effects like harvesting or fire typically induce more pronounced spectral changes compared to those brought by insect defoliations. Nonetheless, detecting processionary moth defoliation is achievable by scrutinizing spectral data from peak feeding periods, coinciding with the winter months, relative to pre or post-peak periods (addressing the “where” and “when” questions). Figure 3 showcases an example illustrating disparities in the normalized difference vegetation index (NDVI) derived from Landsat 8 between pre and peak feeding periods. With the current availability of multispectral sensors on drones the door opens for tailor-made flights featuring substantially enhanced spatial resolutions (as evident in the RGB images from Figure 2). All in all, the suite of remote sensing tools contributes significantly to monitoring insect defoliations in conifer forests, lending robust support to forest management endeavours.
Figure 3. Landsat derived Normalized Difference Vegetation Index (NDVI) differences between autumn 2022 and winter 2023 (center). Drone RGB images of defoliated trees (left and right).
While remote sensing stands as a cornerstone in advancing insect disturbance monitoring, there remains a lot to explore concerning the aforementioned trio of Wh- questions. In this regard, it is noteworthy to emphasize the following challenges:
- Identifying causative agents. Unravelling the exact agent behind a disturbance continues to be a complex endeavour, particularly within diverse and heterogeneous environments.
- Harnessing dense time-series. The potential of employing rich time-series data from remote sensing warrants further exploration to effectively characterize insect disturbances. This pursuit aims to develop tools for near-real-time monitoring that offer heightened accuracy and depth.
- Synthesizing data resolutions. Essential to our comprehension is the integration of data from sources with varying spatial resolutions, such as drones and satellites, allowing us to provide a comprehensive viewpoint that spans from individual trees to entire forest stands.
The mounting availability of tailored insights drawn from drones, coupled with the accessibility to 3D datasets like LiDAR or Structure from Motion (SfM) generated point clouds, in conjunction with the expanding realm of satellite remote sensing data, opens innovative perspectives and notions. These visions materialize into the concept of digital twins, which engenders the creation of dynamic replicas that enable near-real-time forest ecosystem monitoring—a visionary aspiration that is steadily becoming attainable.